# Django 2.0.dev20170216164632 documentation

# PostgreSQL specific aggregation functions

These functions are described in more detail in the PostgreSQL docs.

Note

All functions come without default aliases, so you must explicitly provide one. For example:

    >>> SomeModel.objects.aggregate(arr=ArrayAgg('somefield'))
    {'arr': [0, 1, 2]}

## General-purpose aggregation functions

### `ArrayAgg`

class `ArrayAgg`(expression, distinct=False, **extra)

  Returns a list of values, including nulls, concatenated into an array.

  `distinct`

    An optional boolean argument that determines if array values will be distinct. Defaults to `False`.

### `BitAnd`

class `BitAnd`(expression, **extra)

  Returns an `int` of the bitwise `AND` of all non-null input values, or `None` if all values are null.

### `BitOr`

class `BitOr`(expression, **extra)

  Returns an `int` of the bitwise `OR` of all non-null input values, or `None` if all values are null.

### `BoolAnd`

class `BoolAnd`(expression, **extra)

  Returns `True`, if all input values are true, `None` if all values are null or if there are no values, otherwise `False` .

### `BoolOr`

class `BoolOr`(expression, **extra)

  Returns `True` if at least one input value is true, `None` if all values are null or if there are no values, otherwise `False`.

### `JSONBAgg`

class `JSONBAgg`(expressions, **extra)

  Returns the input values as a `JSON` array. Requires PostgreSQL ≥ 9.5.

### `StringAgg`

class `StringAgg`(expression, delimiter, distinct=False)

  Returns the input values concatenated into a string, separated by the `delimiter` string.

  `delimiter`

    Required argument. Needs to be a string.

  `distinct`

    An optional boolean argument that determines if concatenated values will be distinct. Defaults to `False`.

## Aggregate functions for statistics

### `y` and `x`

The arguments `y` and `x` for all these functions can be the name of a field or an expression returning a numeric data. Both are required.

### `Corr`

class `Corr`(y, x)

  Returns the correlation coefficient as a `float`, or `None` if there aren’t any matching rows.

### `CovarPop`

class `CovarPop`(y, x, sample=False)

  Returns the population covariance as a `float`, or `None` if there aren’t any matching rows.

  Has one optional argument:

  `sample`

    By default `CovarPop` returns the general population covariance. However, if `sample=True`, the return value will be the sample population covariance.

### `RegrAvgX`

class `RegrAvgX`(y, x)

  Returns the average of the independent variable (`sum(x)/N`) as a `float`, or `None` if there aren’t any matching rows.

### `RegrAvgY`

class `RegrAvgY`(y, x)

  Returns the average of the dependent variable (`sum(y)/N`) as a `float`, or `None` if there aren’t any matching rows.

### `RegrCount`

class `RegrCount`(y, x)

  Returns an `int` of the number of input rows in which both expressions are not null.

### `RegrIntercept`

class `RegrIntercept`(y, x)

  Returns the y-intercept of the least-squares-fit linear equation determined by the `(x, y)` pairs as a `float`, or `None` if there aren’t any matching rows.

### `RegrR2`

class `RegrR2`(y, x)

  Returns the square of the correlation coefficient as a `float`, or `None` if there aren’t any matching rows.

### `RegrSlope`

class `RegrSlope`(y, x)

  Returns the slope of the least-squares-fit linear equation determined by the `(x, y)` pairs as a `float`, or `None` if there aren’t any matching rows.

### `RegrSXX`

class `RegrSXX`(y, x)

  Returns `sum(x^2) - sum(x)^2/N` (“sum of squares” of the independent variable) as a `float`, or `None` if there aren’t any matching rows.

### `RegrSXY`

class `RegrSXY`(y, x)

  Returns `sum(x*y) - sum(x) * sum(y)/N` (“sum of products” of independent times dependent variable) as a `float`, or `None` if there aren’t any matching rows.

### `RegrSYY`

class `RegrSYY`(y, x)

  Returns `sum(y^2) - sum(y)^2/N` (“sum of squares” of the dependent variable) as a `float`, or `None` if there aren’t any matching rows.

## Usage examples

We will use this example table:

    | FIELD1 | FIELD2 | FIELD3 |
    |--------|--------|--------|
    |    foo |      1 |     13 |
    |    bar |      2 | (null) |
    |   test |      3 |     13 |

Here’s some examples of some of the general-purpose aggregation functions:

    >>> TestModel.objects.aggregate(result=StringAgg('field1', delimiter=';'))
    {'result': 'foo;bar;test'}
    >>> TestModel.objects.aggregate(result=ArrayAgg('field2'))
    {'result': [1, 2, 3]}
    >>> TestModel.objects.aggregate(result=ArrayAgg('field1'))
    {'result': ['foo', 'bar', 'test']}

The next example shows the usage of statistical aggregate functions. The underlying math will be not described (you can read about this, for example, at wikipedia):

    >>> TestModel.objects.aggregate(count=RegrCount(y='field3', x='field2'))
    {'count': 2}
    >>> TestModel.objects.aggregate(avgx=RegrAvgX(y='field3', x='field2'),
    ...                             avgy=RegrAvgY(y='field3', x='field2'))
    {'avgx': 2, 'avgy': 13}